If nothing else, the data science industry is good at coming up with new, unique, confusing names and terms. ZooKeeper, MapReduce, Hadoop, Pig, Storm, Mahout MongoDB…the list keeps growing and it’s totally understandable if you can’t always identify or explain the different technologies and tools of the industry. In each article in this series we […]
Kafka: What Is it? Read More »
Any serious conversation about the future of data and analytics invariably turns to the topic of artificial intelligence. The past year has seen AI surge in popularity, with high profile corporations and personalities getting behind what many people believe could be the biggest technological watershed moment since the advent of the internet. Indeed, many companies
Artificial intelligence & insurance: how AI is changing the industry Read More »
Last week we offered some suggestions on how to attack your data management initiatives in 2018 according to your organization’s level of data expertise. This week we follow up with some tips on how to pitch those new data science technologies and initiatives to increase your chances of getting resources allocated to your projects. As
Pitching Your 2018 Data Science Budget Read More »
It’s that time of year again! With budgeting for 2018 at the forefront of your agenda, you may be wondering where to head with your data science efforts. Investments in big data are often expensive, but when planned correctly you can manage costs and also ensure that your new systems and employees generate data-driven profits.
Planning Your 2018 Data Science Budget? Here Are Some Logical Projects Read More »
Cyberattacks have become increasingly damaging and visible in recent years, in part because of numerous, high-profile instances of hacking affecting everything from your personal files to global election outcomes. And, in fact, hacking is no longer just about unauthorized access to, or loss of data. As the internet of things continues to expand, so has
How Big Data Helps Detect Hacking Read More »
A key feature of big data is its lack of structure – we’re talking about the stuff that doesn’t fit neatly into excel columns or that is easily described on first glance using numbers or other descriptors. This includes things such as images, satellite data and social media posts, which altogether comprise the bulk of
Biometrics and Big Data: Facial Recognition Read More »
Actuary, data scientist, or just a guy in the rain? The hot new thing, data science, isn’t so new after all. Since the advent of modern actuarial science in the late 1980s, insurance companies have relied on actuaries to use math and statistics to anticipate the future. What Actuaries and Data Scientists have in common
The Difference Between an Actuary and a Data Scientist Read More »
If you have huge volumes of data, chances are that you can get value out of analyzing that data. But, would you derive significant value from analyzing it in real time? This is an important question because, while there are benefits to real time analytics, there are also real costs. To help you decide whether
Do you need real-time business analytics? Start by asking yourself these 4 questions Read More »
There exists a chronic confusion as to what the distinction is between your average software engineer (i.e. programmer) and a data scientist. This is totally understandable, considering the fact that both jobs do involve programming and the term “data science” seems so much like the term “computer science”. However, the two differ in some significant
So, what is the difference between a programmer and a data scientist? Read More »
The data and predictive analytics landscape continues to shift rapidly as old practices are phased out and new technologies enter the mainstream on a virtually constant basis. As these changes continue to play out, business and IT managers face immense challenges when it comes to IT staffing to ensure their teams are comprised of the
Are you taking the right approach to IT staffing? Read More »
